Feature/memory perceptual (#48)

* perf(workflow): pass JSON data to HTTP node as a string

* perf(prompt_opt): simplify log output

* feat(memory): add perceptual memory page API and related database schema

* perf(log): clean up API exception log output

* perf(memory): simplify perceptual memory timeline response by removing metadata

api/app/controllers/memory_perceptual_controller.py

@@ -0,0 +1,255 @@
+import uuid
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy.orm import Session
+
+from app.core.error_codes import BizCode
+from app.core.logging_config import get_api_logger
+from app.core.response_utils import success, fail
+from app.db import get_db
+from app.dependencies import get_current_user
+from app.models import User
+from app.models.memory_perceptual_model import PerceptualType
+from app.schemas.memory_perceptual_schema import (
+    PerceptualQuerySchema,
+    PerceptualFilter
+)
+from app.schemas.response_schema import ApiResponse
+from app.services.memory_perceptual_service import MemoryPerceptualService
+
+api_logger = get_api_logger()
+
+router = APIRouter(
+    prefix="/memory/perceptual",
+    tags=["Perceptual Memory System"],
+    dependencies=[Depends(get_current_user)]
+)
+
+
+@router.get("/{group_id}/count", response_model=ApiResponse)
+def get_memory_count(
+        group_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """Retrieve perceptual memory statistics for a user group.
+
+    Args:
+        group_id: ID of the user group (usually end_user_id in this context)
+        current_user: Current authenticated user
+        db: Database session
+
+    Returns:
+        ApiResponse: Response containing memory count statistics
+    """
+    api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, group_id={group_id}")
+
+    try:
+        service = MemoryPerceptualService(db)
+        count_stats = service.get_memory_count(group_id)
+
+        api_logger.info(f"Memory statistics fetched successfully: total={count_stats.get('total', 0)}")
+
+        return success(
+            data=count_stats,
+            msg="Memory statistics retrieved successfully"
+        )
+
+    except Exception as e:
+        api_logger.error(f"Failed to fetch memory statistics: group_id={group_id}, error={str(e)}")
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg="Failed to fetch memory statistics",
+        )
+
+
+@router.get("/{group_id}/last_visual", response_model=ApiResponse)
+def get_last_visual_memory(
+        group_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """Retrieve the most recent VISION-type memory for a user.
+
+    Args:
+        group_id: ID of the user group
+        current_user: Current authenticated user
+        db: Database session
+
+    Returns:
+        ApiResponse: Metadata of the latest visual memory
+    """
+    api_logger.info(f"Fetching latest visual memory: user={current_user.username}, group_id={group_id}")
+
+    try:
+        service = MemoryPerceptualService(db)
+        visual_memory = service.get_latest_visual_memory(group_id)
+
+        if visual_memory is None:
+            api_logger.info(f"No visual memory found: group_id={group_id}")
+            return success(
+                data=None,
+                msg="No visual memory available"
+            )
+
+        api_logger.info(f"Latest visual memory retrieved successfully: file={visual_memory.get('file_name')}")
+
+        return success(
+            data=visual_memory,
+            msg="Latest visual memory retrieved successfully"
+        )
+
+    except Exception as e:
+        api_logger.error(f"Failed to fetch latest visual memory: group_id={group_id}, error={str(e)}")
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg="Failed to fetch latest visual memory",
+        )
+
+
+@router.get("/{group_id}/last_listen", response_model=ApiResponse)
+def get_last_memory_listen(
+        group_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """Retrieve the most recent AUDIO-type memory for a user.
+
+    Args:
+        group_id: ID of the user group
+        current_user: Current authenticated user
+        db: Database session
+
+    Returns:
+        ApiResponse: Metadata of the latest audio memory
+    """
+    api_logger.info(f"Fetching latest audio memory: user={current_user.username}, group_id={group_id}")
+
+    try:
+        service = MemoryPerceptualService(db)
+        audio_memory = service.get_latest_audio_memory(group_id)
+
+        if audio_memory is None:
+            api_logger.info(f"No audio memory found: group_id={group_id}")
+            return success(
+                data=None,
+                msg="No audio memory available"
+            )
+
+        api_logger.info(f"Latest audio memory retrieved successfully: file={audio_memory.get('file_name')}")
+
+        return success(
+            data=audio_memory,
+            msg="Latest audio memory retrieved successfully"
+        )
+
+    except Exception as e:
+        api_logger.error(f"Failed to fetch latest audio memory: group_id={group_id}, error={str(e)}")
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg="Failed to fetch latest audio memory",
+        )
+
+
+@router.get("/{group_id}/last_text", response_model=ApiResponse)
+def get_last_text_memory(
+        group_id: uuid.UUID,
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """Retrieve the most recent TEXT-type memory for a user.
+
+    Args:
+        group_id: ID of the user group
+        current_user: Current authenticated user
+        db: Database session
+
+    Returns:
+        ApiResponse: Metadata of the latest text memory
+    """
+    api_logger.info(f"Fetching latest text memory: user={current_user.username}, group_id={group_id}")
+
+    try:
+        # 调用服务层获取最近的文本记忆
+        service = MemoryPerceptualService(db)
+        text_memory = service.get_latest_text_memory(group_id)
+
+        if text_memory is None:
+            api_logger.info(f"No text memory found: group_id={group_id}")
+            return success(
+                data=None,
+                msg="No text memory available"
+            )
+
+        api_logger.info(f"Latest text memory retrieved successfully: file={text_memory.get('file_name')}")
+
+        return success(
+            data=text_memory,
+            msg="Latest text memory retrieved successfully"
+        )
+
+    except Exception as e:
+        api_logger.error(f"Failed to fetch latest text memory: group_id={group_id}, error={str(e)}")
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg="Failed to fetch latest text memory",
+        )
+
+
+@router.get("/{group_id}/timeline", response_model=ApiResponse)
+def get_memory_time_line(
+        group_id: uuid.UUID,
+        perceptual_type: Optional[PerceptualType] = Query(None, description="感知类型过滤"),
+        page: int = Query(1, ge=1, description="页码"),
+        page_size: int = Query(10, ge=1, le=100, description="每页大小"),
+        current_user: User = Depends(get_current_user),
+        db: Session = Depends(get_db)
+):
+    """Retrieve a timeline of perceptual memories for a user group.
+
+    Args:
+        group_id: ID of the user group
+        perceptual_type: Optional filter for perceptual type
+        page: Page number for pagination
+        page_size: Number of items per page
+        current_user: Current authenticated user
+        db: Database session
+
+    Returns:
+        ApiResponse: Timeline data of perceptual memories
+    """
+    api_logger.info(
+        f"Fetching perceptual memory timeline: user={current_user.username}, "
+        f"group_id={group_id}, type={perceptual_type}, page={page}"
+    )
+
+    try:
+        query = PerceptualQuerySchema(
+            filter=PerceptualFilter(type=perceptual_type),
+            page=page,
+            page_size=page_size
+        )
+
+        service = MemoryPerceptualService(db)
+        timeline_data = service.get_time_line(group_id, query)
+
+        api_logger.info(
+            f"Perceptual memory timeline retrieved successfully: total={timeline_data.total}, "
+            f"returned={len(timeline_data.memories)}"
+        )
+
+        return success(
+            data=timeline_data.model_dump(),
+            msg="Perceptual memory timeline retrieved successfully"
+        )
+
+    except Exception as e:
+        api_logger.error(
+            f"Failed to fetch perceptual memory timeline: group_id={group_id}, "
+            f"error={str(e)}"
+        )
+        return fail(
+            code=BizCode.INTERNAL_ERROR,
+            msg="Failed to fetch perceptual memory timeline",
+        )

api/app/core/workflow/nodes/http_request/config.py

@@ -73,8 +73,10 @@ class HttpContentTypeConfig(BaseModel):
         content_type = info.data.get("content_type")
         if content_type == HttpContentType.FROM_DATA and not isinstance(v, HttpFormData):
             raise ValueError("When content_type is 'form-data', data must be of type HttpFormData")
-        elif content_type in [HttpContentType.JSON, HttpContentType.WWW_FORM] and not isinstance(v, dict):
+        elif content_type in [HttpContentType.JSON] and not isinstance(v, str):
-            raise ValueError("When content_type is JSON or x-www-form-urlencoded, data must be a object")
+            raise ValueError("When content_type is JSON, data must be of type str")
+        elif content_type in [HttpContentType.WWW_FORM] and not isinstance(v, dict):
+            raise ValueError("When content_type is x-www-form-urlencoded, data must be a object")
         elif content_type in [HttpContentType.RAW, HttpContentType.BINARY] and not isinstance(v, str):
             raise ValueError("When content_type is raw/binary, data must be a string (File descriptor)")
         return v

api/app/core/workflow/nodes/http_request/node.py

@@ -120,7 +120,7 @@ class HttpRequestNode(BaseNode):
                 return {}
             case HttpContentType.JSON:
                 content["json"] = json.loads(self._render_template(
-                    json.dumps(self.typed_config.body.data), state
+                    self.typed_config.body.data, state
                 ))
             case HttpContentType.FROM_DATA:
                 data = {}

api/app/models/memory_perceptual_model.py

@@ -0,0 +1,40 @@
+import datetime
+import uuid
+from enum import IntEnum
+
+from sqlalchemy import Column, ForeignKey, Integer, DateTime, String
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.dialects.postgresql import JSONB
+
+from app.db import Base
+
+
+class PerceptualType(IntEnum):
+    VISION = 1
+    AUDIO = 2
+    TEXT = 3
+    CONVERSATION = 4
+
+
+class FileStorageType(IntEnum):
+    LOCAL = 1
+    REMOTE = 2
+
+
+class MemoryPerceptualModel(Base):
+    __tablename__ = "memory_perceptual"
+
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    end_user_id = Column(UUID(as_uuid=True), ForeignKey("end_users.id"), index=True)
+
+    perceptual_type = Column(Integer, index=True, nullable=False, comment="感知类型")
+
+    storage_service = Column(Integer, default=0, comment="存储服务类型")
+    file_path = Column(String, nullable=False, comment="文件路径")
+    file_name = Column(String, nullable=False, comment="文件名称")
+    file_ext = Column(String, nullable=False, comment="文件后缀名")
+
+    summary = Column(String, comment="摘要")
+    meta_data = Column(JSONB, comment="元信息")
+
+    created_time = Column(DateTime, default=datetime.datetime.now, comment="创建时间")

api/app/repositories/memory_perceptual_repository.py

@@ -0,0 +1,156 @@
+import uuid
+from datetime import datetime
+from typing import List, Tuple, Optional
+
+from sqlalchemy import and_, desc
+from sqlalchemy.orm import Session
+
+from app.core.logging_config import get_db_logger
+from app.models.memory_perceptual_model import MemoryPerceptualModel, PerceptualType, FileStorageType
+from app.schemas.memory_perceptual_schema import PerceptualQuerySchema
+
+db_logger = get_db_logger()
+
+
+class MemoryPerceptualRepository:
+    """Data Access Layer for perceptual memory"""
+
+    def __init__(self, db: Session):
+        self.db = db
+
+    # ==================== Create and update ====================
+    def create_perceptual_memory(
+            self,
+            end_user_id: uuid.UUID,
+            perceptual_type: PerceptualType,
+            file_path: str,
+            file_name: str,
+            file_ext: str,
+            summary: Optional[str] = None,
+            meta_data: Optional[dict] = None,
+            storage_service: FileStorageType = FileStorageType.LOCAL
+
+    ) -> MemoryPerceptualModel:
+
+        """Create perceptual memory"""
+
+        db_logger.debug(f"Creating perceptual memory: end_user_id={end_user_id}, "
+                        f"type={perceptual_type}, file={file_name}")
+
+        try:
+            perceptual_memory = MemoryPerceptualModel(
+                end_user_id=end_user_id,
+                perceptual_type=perceptual_type,
+                storage_service=storage_service,
+                file_path=file_path,
+                file_name=file_name,
+                file_ext=file_ext,
+                summary=summary,
+                meta_data=meta_data,
+                created_time=datetime.now()
+            )
+
+            self.db.add(perceptual_memory)
+            self.db.flush()
+
+            db_logger.info(f"Perceptual memory created successfully: id={perceptual_memory.id}, file={file_name}")
+            return perceptual_memory
+
+        except Exception as e:
+            db_logger.error(f"Failed to create perceptual memory: end_user_id={end_user_id} - {str(e)}")
+            raise
+
+    # ==================== Query ====================
+    def get_count_by_user_id(
+            self,
+            end_user_id: uuid.UUID,
+    ):
+        db_logger.debug(f"Querying perceptual memory Count: end_user_id={end_user_id}")
+
+        try:
+            count = self.db.query(MemoryPerceptualModel).filter(
+                MemoryPerceptualModel.end_user_id == end_user_id
+            ).count()
+            return count
+        except Exception as e:
+            db_logger.error(f"Failed to query perceptual memory count: end_user_id={end_user_id} - {str(e)}")
+            raise
+
+    def get_count_by_type(
+            self,
+            end_user_id: uuid.UUID,
+            perceptual_type: PerceptualType,
+    ):
+        db_logger.debug(f"Querying perceptual memory Count: end_user_id={end_user_id}, type={perceptual_type}")
+
+        try:
+            count = self.db.query(MemoryPerceptualModel).filter(
+                MemoryPerceptualModel.end_user_id == end_user_id,
+                MemoryPerceptualModel.perceptual_type == perceptual_type
+            ).count()
+            return count
+        except Exception as e:
+            db_logger.error(f"Failed to query perceptual memory count: end_user_id={end_user_id} - {str(e)}")
+            raise
+
+    def get_timeline(
+            self,
+            end_user_id: uuid.UUID,
+            query: PerceptualQuerySchema
+    ) -> Tuple[int, List[MemoryPerceptualModel]]:
+        """Get the timeline of a user's perceptual memories"""
+        db_logger.debug(f"Querying perceptual memory timeline: end_user_id={end_user_id}, filter={query.filter}")
+
+        try:
+            base_query = self.db.query(MemoryPerceptualModel).filter(
+                MemoryPerceptualModel.end_user_id == end_user_id
+            )
+
+            if query.filter.type is not None:
+                base_query = base_query.filter(
+                    MemoryPerceptualModel.perceptual_type == query.filter.type
+                )
+
+            total_count = base_query.count()
+
+            memories = base_query.order_by(
+                desc(MemoryPerceptualModel.created_time)
+            ).offset(
+                (query.page - 1) * query.page_size
+            ).limit(query.page_size).all()
+
+            db_logger.info(
+                f"Perceptual memory timeline query succeeded: end_user_id={end_user_id}, total={total_count}, returned={len(memories)}")
+            return total_count, memories
+
+        except Exception as e:
+            db_logger.error(f"Failed to query perceptual memory timeline: end_user_id={end_user_id} - {str(e)}")
+            raise
+
+    def get_by_type(
+            self,
+            end_user_id: uuid.UUID,
+            perceptual_type: PerceptualType,
+            limit: int = 10,
+            offset: int = 0
+    ) -> List[MemoryPerceptualModel]:
+        """Get memories by perceptual type"""
+        db_logger.debug(f"Querying perceptual memories by type: end_user_id={end_user_id}, type={perceptual_type}")
+
+        try:
+            memories = self.db.query(MemoryPerceptualModel).filter(
+                and_(
+                    MemoryPerceptualModel.end_user_id == end_user_id,
+                    MemoryPerceptualModel.perceptual_type == perceptual_type
+                )
+            ).order_by(
+                desc(MemoryPerceptualModel.created_time)
+            ).offset(offset).limit(limit).all()
+
+            db_logger.debug(f"Query by type succeeded: count={len(memories)}")
+            return memories
+
+        except Exception as e:
+            db_logger.error(f"Failed to query perceptual memories by type: end_user_id={end_user_id}, "
+                            f"type={perceptual_type} - {str(e)}")
+            raise

api/app/schemas/memory_perceptual_schema.py

@@ -0,0 +1,133 @@
+import uuid
+from datetime import datetime
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+from app.models.memory_perceptual_model import PerceptualType, FileStorageType
+
+
+class PerceptualFilter(BaseModel):
+    type: PerceptualType | None = Field(
+        default=None,
+        description="Perceptual type used for filtering the query; optional"
+    )
+
+
+class PerceptualQuerySchema(BaseModel):
+    filter: PerceptualFilter = Field(
+        default_factory=lambda: PerceptualFilter(),
+        description="Query filter containing perceptual type criteria"
+    )
+
+    page: int = Field(
+        default=1,
+        ge=1,
+        description="Page number for pagination, starting from 1"
+    )
+
+    page_size: int = Field(
+        default=10,
+        ge=1,
+        le=100,
+        description="Number of records per page, range 1-100"
+    )
+
+
+class PerceptualMemoryItem(BaseModel):
+    """感知记忆项"""
+    id: uuid.UUID = Field(..., description="Unique memory ID")
+    perceptual_type: PerceptualType = Field(..., description="Type of perception, e.g., text, audio, or video")
+    file_path: str = Field(..., description="File path in the storage service")
+    file_name: str = Field(..., description="File name")
+    summary: Optional[str] = Field(None, description="摘要")
+    storage_type: FileStorageType = Field(..., description="Storage type for file")
+    created_time: Optional[datetime] = Field(None, description="创建时间")
+
+    class Config:
+        from_attributes = True
+
+
+class PerceptualTimelineResponse(BaseModel):
+    """感知记忆时间线响应"""
+    total: int = Field(..., description="总数量")
+    page: int = Field(..., description="当前页码")
+    page_size: int = Field(..., description="每页大小")
+    total_pages: int = Field(..., description="总页数")
+    memories: list[PerceptualMemoryItem] = Field(..., description="记忆列表")
+
+    class Config:
+        from_attributes = True
+
+
+# --------------------------
+#  TODO: FileMetaData
+# --------------------------
+class Identity(BaseModel):
+    title: str
+    filename: str
+    source: str  # upload | crawl | system
+    author: Optional[str] = None
+
+
+class Semantic(BaseModel):
+    topic: str
+    domain: str
+    difficulty: str  # beginner | intermediate | advanced
+    intent: str  # informative | instructional | promotional
+    sentiment: str  # positive | neutral | negative
+
+
+class Content(BaseModel):
+    summary: str
+    keywords: list[str]
+    topic: str
+    domain: str
+
+
+class Usage(BaseModel):
+    target_audience: list[str]
+    use_cases: list[str]
+
+
+class Stats(BaseModel):
+    duration_sec: Optional[int] = None
+    char_count: int
+    word_count: int
+
+
+class Processing(BaseModel):
+    transcribed: bool
+    ocr_applied: bool
+    chunked: bool
+    vectorized: bool
+    embedding_model: Optional[str] = None
+
+
+class VideoModal(BaseModel):
+    scene: list[str]
+
+
+class AudioModal(BaseModel):
+    speaker_count: int
+
+
+class TextModal(BaseModel):
+    section_count: int
+
+
+class Asset(BaseModel):
+    type: str
+    modality: str  # text | audio | video
+    format: str  # docx | mp3 | mp4
+    language: str
+    encoding: str
+
+    identity: Identity
+    semantic: Semantic
+    content: Content
+    usage: Usage
+    stats: Stats
+    processing: Processing
+    created_at: str
+    modalities: AudioModal | TextModal | VideoModal

api/app/services/memory_perceptual_service.py

@@ -0,0 +1,166 @@
+import uuid
+from typing import Dict, Any, Optional
+
+from sqlalchemy.orm import Session
+
+from app.core.error_codes import BizCode
+from app.core.exceptions import BusinessException
+from app.core.logging_config import get_business_logger
+from app.models.memory_perceptual_model import PerceptualType, FileStorageType
+from app.repositories.memory_perceptual_repository import MemoryPerceptualRepository
+from app.schemas.memory_perceptual_schema import (
+    PerceptualQuerySchema,
+    PerceptualTimelineResponse,
+    PerceptualMemoryItem,
+    AudioModal, Content, VideoModal, TextModal
+)
+
+business_logger = get_business_logger()
+
+
+class MemoryPerceptualService:
+    def __init__(self, db: Session):
+        self.db = db
+        self.repository = MemoryPerceptualRepository(db)
+
+    def get_memory_count(self, end_user_id: uuid.UUID) -> Dict[str, Any]:
+        """Retrieve perceptual memory statistics for a user."""
+        business_logger.info(f"Fetching perceptual memory statistics: end_user_id={end_user_id}")
+        try:
+            total_count = self.repository.get_count_by_user_id(end_user_id=end_user_id)
+
+            vision_count = self.repository.get_count_by_type(end_user_id, PerceptualType.VISION)
+            audio_count = self.repository.get_count_by_type(end_user_id, PerceptualType.AUDIO)
+            text_count = self.repository.get_count_by_type(end_user_id, PerceptualType.TEXT)
+            conversation_count = self.repository.get_count_by_type(end_user_id, PerceptualType.CONVERSATION)
+
+            stats = {
+                "total": total_count,
+                "by_type": {
+                    "vision": vision_count,
+                    "audio": audio_count,
+                    "text": text_count,
+                    "conversation": conversation_count
+                }
+            }
+
+            business_logger.info(f"Memory statistics fetched successfully: total={total_count}")
+            return stats
+
+        except Exception as e:
+            business_logger.error(f"Failed to fetch memory statistics: {str(e)}")
+            raise BusinessException(f"Failed to fetch memory statistics: {str(e)}", BizCode.DB_ERROR)
+
+    def _get_latest_memory_by_type(
+            self,
+            end_user_id: uuid.UUID,
+            perceptual_type: PerceptualType
+    ) -> Optional[dict[str, Any]]:
+        """Internal helper to retrieve the latest memory by type."""
+        business_logger.info(f"Fetching latest {perceptual_type.name.lower()} memory: end_user_id={end_user_id}")
+        try:
+            memories = self.repository.get_by_type(
+                end_user_id=end_user_id,
+                perceptual_type=perceptual_type,
+                limit=1,
+                offset=0
+            )
+            if not memories:
+                business_logger.info(f"No {perceptual_type.name.lower()} memory found: end_user_id={end_user_id}")
+                return None
+
+            memory = memories[0]
+            meta_data = memory.meta_data or {}
+            modalities = meta_data.get("modalities")
+            content = meta_data.get("content")
+
+            if not modalities:
+                raise BusinessException(f"Modalities not defined, perceptual memory_id={memory.id}", BizCode.DB_ERROR)
+            if not content:
+                raise BusinessException(f"Content not defined, perceptual memory_id={memory.id}", BizCode.DB_ERROR)
+            content = Content(**content)
+            match perceptual_type:
+                case PerceptualType.VISION:
+                    modal = VideoModal(**modalities)
+                case PerceptualType.AUDIO:
+                    modal = AudioModal(**modalities)
+                case PerceptualType.TEXT:
+                    modal = TextModal(**modalities)
+                case _:
+                    raise BusinessException("Unsupported perceptual type", BizCode.DB_ERROR)
+            detail = modal.model_dump()
+
+            result = {
+                "id": str(memory.id),
+                "file_name": memory.file_name,
+                "file_path": memory.file_path,
+                "storage_type": memory.storage_service,
+                "summary": memory.summary,
+                "keywords": content.keywords,
+                "topic": content.topic,
+                "domain": content.domain,
+                "created_time": memory.created_time.isoformat() if memory.created_time else None,
+                **detail
+            }
+
+            business_logger.info(
+                f"Latest {perceptual_type.name.lower()} memory retrieved successfully: file={memory.file_name}")
+            return result
+
+        except Exception as e:
+            business_logger.error(f"Failed to fetch latest {perceptual_type.name.lower()} memory: {str(e)}")
+            raise BusinessException(f"Failed to fetch latest {perceptual_type.name.lower()} memory: {str(e)}",
+                                    BizCode.DB_ERROR)
+
+    def get_latest_visual_memory(self, end_user_id: uuid.UUID) -> Optional[Dict[str, Any]]:
+        return self._get_latest_memory_by_type(end_user_id, PerceptualType.VISION)
+
+    def get_latest_audio_memory(self, end_user_id: uuid.UUID) -> Optional[Dict[str, Any]]:
+        return self._get_latest_memory_by_type(end_user_id, PerceptualType.AUDIO)
+
+    def get_latest_text_memory(self, end_user_id: uuid.UUID) -> Optional[Dict[str, Any]]:
+        return self._get_latest_memory_by_type(end_user_id, PerceptualType.TEXT)
+
+    def get_time_line(self, end_user_id: uuid.UUID, query: PerceptualQuerySchema) -> PerceptualTimelineResponse:
+        """Retrieve a timeline of perceptual memories for a user."""
+        business_logger.info(f"Fetching perceptual memory timeline: "
+                             f"end_user_id={end_user_id}, filter={query.filter}")
+
+        try:
+            if query.page < 1:
+                raise BusinessException("Page number must be greater than 0", BizCode.INVALID_PARAMETER)
+            if query.page_size < 1 or query.page_size > 100:
+                raise BusinessException("Page size must be between 1 and 100", BizCode.INVALID_PARAMETER)
+
+            total_count, memories = self.repository.get_timeline(end_user_id, query)
+
+            memory_items = []
+            for memory in memories:
+                memory_item = PerceptualMemoryItem(
+                    id=memory.id,
+                    perceptual_type=PerceptualType(memory.perceptual_type),
+                    file_path=memory.file_path,
+                    file_name=memory.file_name,
+                    summary=memory.summary,
+                    created_time=memory.created_time,
+                    storage_type=FileStorageType(memory.storage_service),
+                )
+                memory_items.append(memory_item)
+
+            timeline_response = PerceptualTimelineResponse(
+                total=total_count,
+                page=query.page,
+                page_size=query.page_size,
+                total_pages=(total_count + query.page_size - 1) // query.page_size,
+                memories=memory_items
+            )
+
+            business_logger.info(f"Perceptual memory timeline retrieved successfully: "
+                                 f"total={total_count}, returned={len(memories)}")
+            return timeline_response
+
+        except BusinessException:
+            raise
+        except Exception as e:
+            business_logger.error(f"Failed to fetch perceptual memory timeline: {str(e)}")
+            raise BusinessException(f"Failed to fetch perceptual memory timeline: {str(e)}", BizCode.DB_ERROR)

api/app/services/prompt_optimizer_service.py

@@ -166,6 +166,8 @@ class PromptOptimizerService:
         model_config = self.get_model_config(tenant_id, model_id)
         session_history = self.get_session_message_history(session_id=session_id, user_id=user_id)
 
+        logger.info(f"Prompt optimization started, user_id={user_id}, session_id={session_id}")
+
         # Create LLM instance
         api_config: ModelApiKey = model_config.api_keys[0]
         llm = RedBearLLM(RedBearModelConfig(
@@ -203,7 +205,6 @@ class PromptOptimizerService:
 
         messages.extend(session_history[:-1])  # last message is current message
         messages.extend([(RoleType.USER.value, rendered_user_message)])
-        logger.info(f"Prompt optimization message: {messages}")
         buffer = ""
         prompt_started = False
         prompt_finished = False
@@ -250,6 +251,7 @@ class PromptOptimizerService:
             content=desc
         )
         variables = self.parser_prompt_variables(optim_result.get("prompt"))
+        logger.info(f"Prompt optimization completed, user_id={user_id}, session_id={session_id}")
         yield {"desc": optim_result.get("desc"), "variables": variables}
 
     @staticmethod